AI & Machine Learning Engineer

What you’ll do

• Design, build, and maintain robust data pipelines to collect, clean, and transform data from multiple sources for analysis, modeling, and operational deployments.
• Develop and implement ML models and algorithms through the full life cycle, including problem framing, data collection, preparation, feature engineering, model selection, training, evaluation, deployment, retraining, and ongoing advancement.
• Design and build AI agents that execute workflows inside enterprise systems such as databases, CRMs, ticketing, and knowledge bases, deployed with reliable and safety guardrails.
• Build end-to-end agent orchestration including prompting, memory/state, tool-calling, retries and fallbacks, and create evaluation frameworks using test suites, simulations, and human-in-the-loop review to improve accuracy and reduce errors.
• Develop RAG GPT applications by integrating enterprise knowledge sources (documents and databases) with embeddings, vector search, and prompt orchestration for grounded responses, supported by evaluation and safety guardrails.
• Analyze large datasets to identify trends, patterns, and actionable insights, and create visualizations and reports for stakeholders.
• Monitor and evaluate model and system performance, then make adjustments to optimize accuracy and efficiency.
• Document processes, methodologies, and model development for transparency and reproducibility.
• Provide training and support to other team members or departments on data tools, techniques, and best practices.
• Collaborate with internal IT teams to ensure infrastructure supports stable, highly available, and well-maintained Data Science and AI applications.
• Stay current with emerging technologies and industry trends to continually improve data engineering practices and contribute to cutting-edge solutions.
• Ensure data accuracy, consistency, and security, implementing and enforcing data governance policies and best practices.

What you bring

• 5+ years of experience in data engineering, data science, or a related role, including hands-on experience building and deploying machine learning models.
• A Bachelor’s degree in Computer Science, Analytics, or a related field from an accredited college or university; Master of Science preferred.
• Advanced proficiency in Python and common ML/data libraries including scikit-learn, TensorFlow, Keras, PyTorch, Pandas, and NumPy.
• Strong knowledge of machine learning methodologies, including supervised learning (regression, classification) and unsupervised learning (clustering, dimensionality reduction, anomaly detection).
• Strong SQL skills, including designing and querying relational databases and supporting data warehousing; familiarity with ETL/ELT workflows and tools such as SSIS or equivalent.
• Working knowledge of medallion architectures.
• Experience with cloud-based ML development and deployment on AWS, Azure, or Google Cloud.
• Proficiency with version control and collaborative workflows including Git, branching strategies, code review, and basic CI/CD concepts.
• Expertise in probability and statistics, including experimental design, hypothesis testing, uncertainty modeling, performance measurement, and evaluation metric selection.
• Experience building AI model-powered applications using model APIs, including prompt design, tool/function calling, structured outputs (JSON), and response validation/guardrails.
• Strong understanding of RAG architectures including ingestion pipelines, chunking strategies, metadata design, embeddings, and retrieval methods.
• Hands-on experience with vector databases/search systems, including tuning for relevance, latency, and cost.

Environment & physical requirements

• Work involves periodic moderately physically demanding tasks, including lifting, carrying, pushing, and/or pulling moderately heavy objects and materials (up to 25 pounds), with assistance/equipment for heavier moves.
• May involve climbing, stooping, kneeling, crouching, or crawling; must be able to safely operate assigned vehicles, possibly long distances.
• Regularly performed inside and/or outside with potential exposure to adverse conditions such as inclement weather, atmospheric elements, and pathogenic substances.
• Noise level in the work environment is usually moderate.